Generation adaptive filtering for subsampling component video as input to a nonlinear editing system

ABSTRACT

If graphics data is imported into and then exported from a nonlinear editing system several times, image quality tends to degrade due to multiple filtering operations applied to the graphics data on import. Sometimes, a portion of graphics data imported into a nonlinear editing system has been processed by a nonlinear editing system, whereas another portion has not. To avoid such degradation of image quality, portions of graphics data that have been previously filtered and decimated are identified and not filtered prior to decimation. Other portions of graphics data are filtered and decimated.

BACKGROUND

Nonlinear editing systems commonly process video that is stored in asubsampled component video format. There are a number of subsampledcomponent video formats, such as YCrCb 4:2:2 video data. Data in thisformat may be received as RGB component video data that is convertedinto YCrCb data in a 4:4:4 format, which is then decimated to thesubsampled 4:2:2 format.

Graphics data that is imported into a nonlinear editing system commonlyis in an RGB component video data format. To avoid various visibleartifacts in an image, due particularly to aliasing, graphics datacommonly is filtered prior to decimation.

Graphics data also is commonly exported from a nonlinear editing systemfrom the subsampled YCrCb 4:2:2 format into the RGB component video dataformat. This export process involves interpolating or filtering thedecimated data to produce the RGB component video data.

SUMMARY

If graphics data is imported into and then exported from a nonlinearediting system several times, image quality tends to degrade due tomultiple filtering operations applied to the graphics data on import.Sometimes, a portion of graphics data imported into a nonlinear editingsystem has been processed by a nonlinear editing system, whereas anotherportion has not. To avoid such degradation of image quality, portions ofgraphics data that have been previously filtered and decimated areidentified and not filtered prior to decimation. Other portions ofgraphics data are filtered and decimated.

Accordingly, in one aspect, an image in a first color representation isimported into digital nonlinear video editing system that uses images ina second subsampled color representation by detecting in a portion ofthe image in the first color representation a signature of an upsamplingfilter for generating an image in the first color representation from animage in the second subsampled color representation. If the signature isdetected, the portion of the image in the first color representation isdecimated without filtering the portion of the image. If the signatureis not detected, the portion of the image in the first colorrepresentation is filtered before decimating the portion of the image.

In another aspect, an image in a first color representation is importedinto digital nonlinear video editing system that uses images in a secondsubsampled color representation by determining for each pixel in animage in the second color representation, whether a correspondingportion of the image in the first color representation was filteredusing an upsampling filter for generating an image in the first colorrepresentation from an image in the second subsampled colorrepresentation. If the portion of the image in the first colorrepresentation was filtered using the upsampling filter, the portion ofthe image is decimated without filtering the portion of the image. Ifthe portion of the image in the first color representation was notfiltered using the upsampling filter, the portion of the image isfiltered before decimating the portion of the image.

Another aspect is a method or system for determining a measure oflikelihood that an image in a first color representation was filteredusing an upsampling filter used by a digital nonlinear video editingsystem to generate an image in the first color representation from animage in a second subsampled color representation. A set of pixels inthe image in the first color representation is selected according to thesecond subsampled color representation. A value for each of a pluralityof pixels intermediate the selected pixels is estimated. The estimatedvalue is compared to an actual value for the plurality of pixelsintermediate the selected pixels in the image in the first colorrepresentation.

Another aspect is a method or system for generation adaptive filteringan image in a first color representation during conversion of the imageto a second subsampled color representation used in a digital nonlinearvideo editing system. Information is received that is indicative of, foreach of a plurality of selected pixels in the image in the first colorrepresentation, whether the portion of the image around the selectedpixel was filtered using an upsampling filter used by the digitalnonlinear video editing system to generate an image in the first colorrepresentation from an image in the second subsampled colorrepresentation. Each of the selected pixels of the image in the firstcolor representation is selectively filtered and decimated according towhether the portion of the image around the pixel was filtered using theupsampling filter.

Another aspect is a digital information product that includes a computerreadable medium and information stored on the computer readable mediumthat, when interpreted by a computer, indicates, for each of a pluralityof selected pixels in an image in a first color representation, whetherthat the portion of the image around the selected pixel was filteredusing an upsampling filter used by a digital nonlinear video editingsystem to generate an image in the first color representation from animage in a second subsampled color representation.

In the various foregoing aspects, detection may be performed bydetermining a measure of likelihood that an image in a first colorrepresentation was filtered using an upsampling filter used by a digitalnonlinear video editing system to generate an image in the first colorrepresentation from an image in a second subsampled colorrepresentation. The measure of likelihood may be determined by selectinga set of pixels in the image in the first color representation accordingto the second subsampled color representation, estimating a value foreach of a plurality of pixels intermediate the selected pixels, andcomparing the estimated value to an actual value for the plurality ofpixels intermediate the selected pixels in the image in the first colorrepresentation. Such comparison may be performed by determining whetherthe estimated value is within a range defined around the actual value.In the various foregoing aspects, filtering may be performed by applyinga tent filter to pixel data comprising the portion of the image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example generation adaptive filter;

FIG. 2 is a flow chart describing operation of a generation adaptivefilter;

FIGS. 3A-3D are graphs illustrating how a portion of an image may beanalyzed to detect whether the portion had been previously filtered;

FIG. 4 is a flow chart describing operation of an example implementationof an upsampling detection module; and

FIG. 5 is a flow chart describing more details of operations in FIG. 4.

DETAILED DESCRIPTION

A nonlinear editing system allows sequences of segments of video, audioand other data stored on a random access computer readable medium to becombined into a temporal presentation.

A nonlinear editing system generally has a color representation in whichvideo data is formatted for processing. The color representation may bered, green and blue components, or luminance and chroma components, foreach pixel in the video data. Each component value generally isrepresented by a number of bits, such as 8 bits or 10 bits.

A nonlinear editing system commonly processes video that is stored in asubsampled component video format. A component video format issubsampled if one or more of the components is sampled at a frequencylower than the frequency at which the other component is sampled. Forexample, the luminance component may be sampled for each pixel, whereasthe chrominance components may be sampled every other pixel. An exampleof such a format is called YCrCb 4:2:2 video data. Other kinds ofsubsampled component video formats include, but are not limited to,4:1:1, 4:2:0, YUV9 and 4:1:0. In some formats, the subsampling performedis adaptive.

A nonlinear editing system commonly receives data in another format,such as RGB video data that is not subsampled. This other video data isconverted by the nonlinear editing system into YCrCb data in a 4:4:4format, which is then decimated to the subsampled 4:2:2 format forprocessing. If exported from the system into RGB format, the subsampleddata is upsampled. Upsampling commonly is performed by using simplelinear interpolation, but any other kind of interpolation filter may beused.

If the incoming data has been previously subsampled and upsampled, it issometimes possible to identify in the upsampled data where theupsampling occurred, with some level of certainty. In other words, theupsampling process leaves a sort of signature in the image data that canbe detected. A measure of likelihood that an image or portion of animage was previously upsampled can be determined. To avoid degradationof image quality, portions of graphics data that have been previouslyfiltered and decimated are identified and not filtered prior todecimation. Such degradation often occurs with graphics that have highfrequencies in the chroma components. Other portions of graphics dataare filtered and decimated. This process is referred to herein asgeneration adaptive filtering.

FIG. 1 is a block diagram of an example generation adaptive filter. Anupsampling detection module 102, an implementation of which is describedin more detail below in connection with FIGS. 3-5, processes an image100 to detect whether at least a portion of the image was previouslyupsampled. For example, the upsampling detection module may output ameasure of likelihood that the portion of the image was previouslyupsampled. The output of the upsampling detection module 102 is thedetection result 104. The detection result 104 is applied to a controlinput of a selector 106 that receives the image data 100 at an input108. The control input to the selector 106 configures the selector so asto control whether the image data 100 passes to output 110 or to output112. If passed to output 112, the image data is filtered by filter 114,examples of which are described below. The unfiltered image data ispassed to input 116 of a selector 120 whereas the filtered image data ispassed to input 118 of the selector 120. Selector 120 also is controlledby the detection result 104. The image data output by the selector 120is decimated by decimator 122. Although selectors 106 and 120 are shownas two selectors in FIG. 1, such selectors may be one selector,particularly if implemented as part of a computer program. The selectors106 and 120 thus controls flow of image data according to the receivedinformation such that the portion of the image is decimated withoutfiltering the portion of the image if the portion of the image in thefirst color representation was filtered using the upsampling filter, andthe portion of the image is filtered before decimating the portion ofthe image if the portion of the image in the first color representationwas not filtered using the upsampling filter.

FIG. 2 is a flow chart describing operation of a generation adaptivefilter. The image is evaluated 200 to detect whether any portion of theimage had been previously upsampled, in a manner described below. If theimage was previously upsampled, determined in step 202, the image isdecimated 204. If the image was not previously upsampled, the image isfiltered 206, in a manner described below, then decimated 204.

FIGS. 3A-3D are graphs illustrating how a portion of an image may beanalyzed to detect whether the portion had been previously filtered. Theoperation of an example implementation of upsampling detection module102 (FIG. 1) that may use this technique is described below inconnection with FIGS. 4-5. In particular, this process is based on aprinciple that if a set of samples of image data is upsampled to createa set of samples of interpolated image data, the interpolated sampleshave values that are known for a given interpolation filter. A set ofsamples may be selected from the input image data that would have beenused to create an interpolated sample by a given interpolation filter ifthe input image data was previously upsampled.

In FIG. 3A, a set of samples 300 of one component of several pixels ofan image is shown having magnitude C along the vertical axis and time Xalong the horizontal axis. The samples shown in FIG. 3A have not yetbeen subsampled. If these samples were obtained by upsampling, thensamples 302 were used by an interpolation filter to obtain samples 304and 306. (In this example, the use of a linear interpolation filter isassumed). The set of samples 302 are applied to the interpolation filterto obtain estimated upsampled data 308 and 310, as shown in FIG. 3B. Ifone of these interpolated values 308 and 310 is outside a threshold 314,316 (both above and below) of the actual corresponding values 304 and306 in the original set of samples 300, as shown in FIG. 3C, the portionof the input image data around the pixel at time=0 in the graph likelywas not previously subsampled and upsampled. The chrominance values inthis portion of the image then are filtered, an example result of whichis shown in FIG. 3D. In FIG. 3D, the output samples 318, 320, 324 and326 are essentially unchanged from the input samples, but output sample322 has been changed from the corresponding input sample. The filteredoutput samples are then subsampled to provide the subsampled image data.

The example in FIGS. 3A-3D illustrates one estimated value beingcompared to one actual value. In YCrCb 4:2:2 data, the distance, such asthe Euclidean distance or squared Euclidean distance, between theestimated values for the two chrominance components and the actualvalues for the two chrominance components at a particular sample timemay be computed and compared to a threshold.

FIG. 4 is a flowchart describing in more detail an exampleimplementation of such an upsampling detection module. This processevaluates the input image pixel by pixel. First, the values for thechrominance components of each pixel in an image (received in, forexample, an RGB format) are computed 400. A pixel is then selected 402and the values of its chrominance components are obtained. The first twoand last two pixels of the image can be omitted. The values of thechrominance components of the previous two and subsequent two pixels arethen obtained 404. The values of chrominance components of the previousand subsequent pixels then are estimated 406. The estimated values arecompared 408 to the actual values. According to the comparison, if theimage portion is to be filtered, as determined at 410, the chrominancevalues of pixels in the image around the selected pixel are filtered412. After filtering, the filtered output image data may be provided asa filtered image in YUV component values or in RGB component values, orany other component values. Otherwise, or after filtering, the nextpixel, if any, is selected 414 and processing continues with step 404.When the process of FIG. 4 completes filtering the image, thechrominance components of pixels the resulting image data may bedecimated.

More details of the process shown in FIG. 4 will now be described inconnection with FIG. 5. In particular, for a selected pixel (havingchrominance values u₀, v₀), estimated chrominance values for theimmediately preceding and subsequent pixels are computed 500 using itsneighboring pixels at sampling times −2 and +2, according to theinterpolation filter being used for upsampling. For example, linearinterpolation (an arithmetic mean) between u⁻², v⁻² and u₀, v₀ can beused to compute the estimated values u⁻¹, v⁻¹, and between u₀, v₀ andu₂, v₂ can be used to compute the estimated values u₁, v₁. The error c⁻¹between the estimated values for u⁻¹, v⁻¹ and the actual values arecomputed 502, for example using a Euclidean distance or squaredEuclidean distance. The error c₁ between the estimated values for u₁, v₁and the actual values are computed 504, for example using a Euclideandistance or squared Euclidean distance. If either c₁ or c⁻¹ is greaterthan a specified threshold, as determined in 506 or 508, then thechrominance values of the selected pixel are filtered 510 before thevalues of the next pixel and its adjacent pixels are obtained 512. Forexample, a 1:2:1 tent filter may be used to compute the filtered valuesu_(f0), v_(f0) from u⁻¹, v⁻¹, u₀, v₀ and u₁, v₁, by:

u _(f0)=(u ⁻¹+2*u ₀ +u ₁)/4

v _(f0)=(v ⁻¹+2*v ₀ +v ₁)/4

The comparison in step 408, and the error computation in steps 502 and504 may be performed by computing the distance between the pairs ofchrominance values, for example by using a distance metric such as aEuclidean distance or squared Euclidean distance. The distance is thencompared to a threshold. The actual threshold selected involves a tradeoff. If the threshold is tight, more filtering of the input image likelyis to occur, but better image quality may be obtained. However, if thethreshold is more forgiving, less filtering of the input image likely isto occur, but some generational loss of image quality may result. Thecomparison to the threshold may be performed, for example, by comparingthe calculated distance to the threshold by using a “less than” or a“less than or equal to” comparison operation.

Clipping also may occur on image data in the RGB color space. Inparticular, if data is upsampled, filtering occurs only on the chromasamples of the YCrCb values prior to conversion to RGB values, whereasthe luma stays the same. However, the luma may be changed if theresulting RGB values are outside of the range of legal RGB values(16-235) in the ITU 601 standard. If the values are clipped, upsamplingdetection may not work if a tight threshold is used. A threshold of+/−20 (an integer value in RGB space) for 8-bit samples for comparisonto a Euclidean distance metric (or 400 for a squared Euclidean distancemetric) appears to work well.

A result of upsampling detection that can be retained is information,for each pixel of an image, of the likelihood that the portion of theimage around the pixel was previously upsampled. A matrix of values maybe used to represent all of these values for an image. This data couldbe stored as metadata along with an image to be used for other kinds ofprocessing. This information may be used, for example, to indicatewhether the image, or a portion of it, was previously in the system.

Many kinds of filters may be used to implement filter 114 in FIG. 1 orto perform the filtering step 206 in FIG. 2. The filter may be, forexample, a half-band FIR filter. Characteristics of a good filter forchroma down-sampling would reduce aliasing by attenuating thefrequencies above the half band, and would keep the weights positive toavoid undershoots and overshoots, and would use a relatively smallnumber of taps, e.g., less than seven, to reduce computation time in theupsampling detection module and the filter. Only the chroma values arefiltered, not the luminance values. A 3-tap tent filter (with 1:2:1 asvalues) and a 5-tap Gaussian filter (with 2:15:30:15:2 as values) areexamples of suitable implementations of the filter. Larger filterkernels show marginally better results but significantly lowerperformance. The use of a narrow tent filter reduces the extent to whichthe image is softened, but provides sufficient filtering to avoidintroduction of artifacts in an image that may occur after multiplefiltering and upsampling operations.

It also is possible to have a thresholding operation that acts as a softthreshold instead of a hard threshold. It such an implementation, twothresholds may be used. If the difference between the estimated valueand the actual value is less than a first threshold, filtering is notperformed. If the difference (D) between the estimated value and theactual value is greater than the first threshold (FT) but less than asecond threshold (ST), an operation may be performed to combine both afiltered image and the unfiltered image. For example, a blend of thesetwo images may be performed according to the following function:

New value=A(filtered pixel value)+(1−A)(unfiltered pixel value),

where A is an alpha value equal to (D−FT)/(ST−FT). If the differencebetween the estimated value and the actual value is greater than thesecond threshold, filtering is performed.

Having now described an example embodiment, it should be apparent tothose skilled in the art that the foregoing is merely illustrative andnot limiting, having been presented by way of example only. Numerousmodifications and other embodiments are within the scope of one ofordinary skill in the art and are contemplated as falling within thescope of the invention.

What is claimed is:
 1. A method for importing an image in a first colorrepresentation into digital nonlinear video editing system that usesimages in a second subsampled color representation, comprising:detecting in a portion of the image in the first color representation asignature of an upsampling filter for generating an image in the firstcolor representation from an image in the second subsampled colorrepresentation; if the signature is detected, decimating the portion ofthe image in the first color representation without filtering theportion of the image; and if the signature is not detected, filteringthe portion of the image in the first color representation beforedecimating the portion of the image, wherein detecting comprisesdetermining a measure of likelihood that an image in a first colorrepresentation was filtered using an upsampling filter used by a digitalnonlinear video editing system to generate an image in the first colorrepresentation from an image in a second subsampled colorrepresentation, and wherein determining the measure of likelihoodcomprises: selecting a set of pixels in the image in the first colorrepresentation according to the second subsampled color representation;estimating a value for each of a plurality of pixels intermediate theselected pixels; and comparing the estimated value to an actual valuefor the plurality of pixels intermediate the selected pixels in theimage in the first color representation.
 2. The method of claim 1,wherein filtering comprises applying a tent filter to pixel datacomprising the portion of the image.
 3. The method of claim 1, whereincomparing comprises: determining whether the estimated value is within arange defined around the actual value.
 4. A method for importing animage in a first color representation into digital nonlinear videoediting system that uses images in a second subsampled colorrepresentation, comprising: detecting in a portion of the image in thefirst color representation a signature of an upsampling filter forgenerating an image in the first color representation from an image inthe second subsampled color representation; if the signature isdetected, decimating the portion of the image in the first colorrepresentation without filtering the portion of the image; and if thesignature is not detected, filtering the portion of the image in thefirst color representation before decimating the portion of the image,wherein detecting comprises: selecting a set of pixels in the image inthe first color representation according to the second subsampled colorrepresentation; estimating a value for each of a plurality of pixelsintermediate the selected pixels; and comparing the estimated value toan actual value for the plurality of pixels intermediate the selectedpixels in the image in the first color representation.
 5. The method ofclaim 4, wherein comparing comprises: determining whether the estimatedvalue is within a range defined around the actual value.
 6. The methodof claim 4, wherein filtering comprises applying a tent filter to pixeldata comprising the portion of the image.
 7. A system for importing animage in a first color representation into digital nonlinear videoediting system that uses images in a second subsampled colorrepresentation, comprising: means for detecting in a portion of theimage in the first color representation a signature of an upsamplingfilter for generating an image in the first color representation from animage in the second subsampled color representation; means fordecimating the portion of the image in the first color representationwithout filtering the portion of the image if the signature is detected;and means for filtering the portion of the image in the first colorrepresentation before decimating the portion of the image if thesignature is not detected, wherein the means for detecting comprises:means for selecting a set of pixels in the image in the first colorrepresentation according to the second subsampled color representation;means for estimating a value for each of a plurality of pixelsintermediate the selected pixels; and means for comparing the estimatedvalue to an actual value for the plurality of pixels intermediate theselected pixels in the image in the first color representation.
 8. Thesystem of claim 7, wherein the means for comparing comprises means fordetermining whether the estimated value is within a range defined aroundthe actual value.
 9. The system of claim 7, wherein means for filteringcomprises means for applying a tent filter to pixel data comprising theportion of the image.
 10. A system for importing an image in a firstcolor representation into digital nonlinear video editing system thatuses images in a second subsampled color representation, comprising: anupsampling detection module having an input for receiving a portion ofthe image in the first color representation and an output for providingdetection results indicative of whether a signature was detected of anupsampling filter used for generating an image in the first colorrepresentation from an image in the second subsampled colorrepresentation; a filter having an input for receiving a portion of theimage and an output for providing filtered image data; a decimatorhaving an input for receiving image data and an output for providingsubsampled image data; and one or more selectors having an input forreceiving the detection results and controlling flow of image data suchthat the portion of the image in the first color representation isdecimated by the decimator without filtering the portion of the image ifthe signature is detected, and the portion of the image in the firstcolor representation is filtered before decimating the portion of theimage if the signature is not detected, wherein the upsampling detectionmodule comprises: means for selecting a set of pixels in the image inthe first color representation according to the second subsampled colorrepresentation; means for estimating a value for each of a plurality ofpixels intermediate the selected pixels; and means for comparing theestimated value to an actual value for the plurality of pixelsintermediate the selected pixels in the image in the first colorrepresentation.
 11. The system of claim 10, wherein the means forcomparing comprises means for determining whether the estimated value iswithin a range defined around the actual value.
 12. The system of claim10, wherein the filter comprises a tent filter.
 13. A method forimporting an image in a first color representation into digitalnonlinear video editing system that uses images in a second subsampledcolor representation, comprising: for each pixel in an image in thesecond color representation, determining whether a corresponding portionof the image in the first color representation was filtered using anupsampling filter for generating an image in the first colorrepresentation from an image in the second subsampled colorrepresentation; if the portion of the image in the first colorrepresentation was filtered using the upsampling filter, decimating theportion of the image without filtering the portion of the image; and ifthe portion of the image in the first color representation was notfiltered using the upsampling filter, filtering the portion of the imagebefore decimating the portion of the image, wherein determining whetherthe corresponding portion of the image in the first color representationwas filtered using the upsampling filter comprises: selecting a set ofpixels in the image in the first color representation according to thesecond subsampled color representation; estimating a value for each of aplurality of pixels intermediate the selected pixels; and comparing theestimated value to an actual value for the plurality of pixelsintermediate the selected pixels in the image in the first colorrepresentation.
 14. The method of claim 13, wherein filtering comprisesapplying a tent filter to pixel data comprising the portion of theimage.
 15. The method of claim 13, wherein comparing comprises:determining whether the estimated value is within a range defined aroundthe actual value.
 16. A method for importing an image in a first colorrepresentation into digital nonlinear video editing system that usesimages in a second subsampled color representation, comprising: for eachpixel in an image in the second color representation, determiningwhether a corresponding portion of the image in the first colorrepresentation was filtered using an upsampling filter for generating animage in the first color representation from an image in the secondsubsampled color representation; if the portion of the image in thefirst color representation was filtered using the upsampling filter,decimating the portion of the image without filtering the portion of theimage; and if the portion of the image in the first color representationwas not filtered using the upsampling filter, filtering the portion ofthe image before decimating the portion of the image, whereindetermining comprises determining a measure of likelihood that an imagein a first color representation was filtered using an upsampling filterused by a digital nonlinear video editing system to generate an image inthe first color representation from an image in a second subsampledcolor representation, wherein determining the measure of likelihoodcomprises: selecting a set of pixels in the image in the first colorrepresentation according to the second subsampled color representation;estimating a value for each of a plurality of pixels intermediate theselected pixels; and comparing the estimated value to an actual valuefor the plurality of pixels intermediate the selected pixels in theimage in the first color representation.
 17. The method of claim 16,wherein comparing comprises: determining whether the estimated value iswithin a range defined around the actual value.
 18. The method of claim16, wherein filtering comprises applying a tent filter to pixel datacomprising the portion of the image.
 19. A system for importing an imagein a first color representation into digital nonlinear video editingsystem that uses images in a second subsampled color representation,comprising: means for determining, for each pixel in an image in thesecond color representation, whether a corresponding portion of theimage in the first color representation was filtered using an upsamplingfilter for generating an image in the first color representation from animage in the second subsampled color representation; means fordecimating the portion of the image without filtering the portion of theimage if the portion of the image in the first color representation wasfiltered using the upsampling filter; and means for filtering theportion of the image before decimating the portion of the image if theportion of the image in the first color representation was not filteredusing the upsampling filter; wherein the means for determiningcomprises: means for selecting a set of pixels in the image in the firstcolor representation according to the second subsampled colorrepresentation; means for estimating a value for each of a plurality ofpixels intermediate the selected pixels; and means for comparing theestimated value to an actual value for the plurality of pixelsintermediate the selected pixels in the image in the first colorrepresentation.
 20. The system of claim 19, wherein the means forcomparing comprises means for determining whether the estimated value iswithin a range defined around the actual value.
 21. The system of claim19, wherein the means for filtering comprises a tent filter.
 22. Asystem for importing an image in a first color representation intodigital nonlinear video editing system that uses images in a secondsubsampled color representation, comprising: an upsampling detectorhaving an input for receiving at least a portion of the image, andhaving an output for providing and indication, for each pixel in animage in the second color representation, whether a correspondingportion of the image in the first color representation was filteredusing an upsampling filter used for generating an image in the firstcolor representation from an image in the second subsampled colorrepresentation; a filter having an input for receiving a portion of theimage and an output for providing filtered image data; a decimatorhaving an input for receiving image data and an output for providingsubsampled image data; and one or more selectors having an input forreceiving the detection results and controlling flow of image data suchthat the portion of the image is decimated without filtering the portionof the image if the portion of the image in the first colorrepresentation was filtered using the upsampling filter, and the portionof the image is filtered before decimating the portion of the image ifthe portion of the image in the first color representation was notfiltered using the upsampling filter, wherein the upsampling detectorcomprises: means for selecting a set of pixels in the image in the firstcolor representation according to the second subsampled colorrepresentation; means for estimating a value for each of a plurality ofpixels intermediate the selected pixels; and means for comparing theestimated value to an actual value for the plurality of pixelsintermediate the selected pixels in the image in the first colorrepresentation.
 23. The system of claim 22, wherein the means forcomparing comprises means for determining whether the estimated value iswithin a range defined around the actual value.
 24. The system of claim22, wherein the filter comprises a tent filter.
 25. A method fordetermining a measure of likelihood that an image in a first colorrepresentation was filtered using an upsampling filter used by a digitalnonlinear video editing system to generate an image in the first colorrepresentation from an image in a second subsampled colorrepresentation, comprising: selecting a set of pixels in the image inthe first color representation according to the second subsampled colorrepresentation; estimating a value for each of a plurality of pixelsintermediate the selected pixels; and comparing the estimated value toan actual value for the plurality of pixels intermediate the selectedpixels in the image in the first color representation.
 26. The method ofclaim 25, wherein comparing comprises: determining whether the estimatedvalue is within a range defined around the actual value.
 27. A systemfor determining a measure of likelihood that an image in a first colorrepresentation was filtered using an upsampling filter used by a digitalnonlinear video editing system to generate an image in the first colorrepresentation from an image in a second subsampled colorrepresentation, comprising: means for selecting a set of pixels in theimage in the first color representation according to the secondsubsampled color representation; means for estimating a value for eachof a plurality of pixels intermediate the selected pixels; and means forcomparing the estimated value to an actual value for the plurality ofpixels intermediate the selected pixels in the image in the first colorrepresentation.
 28. The system of claim 27, wherein means for comparingcomprises means for determining whether the estimated value is within arange defined around the actual value.